{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d1f20875-c50d-46df-906b-49ec1d0c6002",
   "metadata": {},
   "source": [
    "# 6.4 使用一元逻辑回归实现商品房分类"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c90a18b9-d8e8-431c-856c-35f1e4ed05e1",
   "metadata": {},
   "source": [
    "### 1.任务描述\n",
    "\n",
    "一组房屋面积的数据如下（16个）:\n",
    "\n",
    "137.97,104.50,100.00,124.32,79.20,99.00,124.00,114.00,106.69,138.05,53.75,46.91,68.00,63.02,81.26,86.21。\n",
    "\n",
    "与房屋面积对应的房屋类型如下：\n",
    "\n",
    "1,1,0,1,0,1,1,0,0,1,0,0,0,0,0,0\n",
    "\n",
    "其中，1表示高档住宅；0表示普通住宅。\n",
    "\n",
    "要求：\n",
    "\n",
    "- 创建一元逻辑回归模型\n",
    "- 使用交叉熵损失函数计算损失，训练模型，得到模型参数\n",
    "- 绘制Sigmoid曲线\n",
    "- 使用模型对一个新的商品房（110平方米）进行分类"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "f5b4fc39-cbcf-432a-bf1e-e75e642d4b87",
   "metadata": {},
   "source": [
    "### 2.知识准备\n",
    "\n",
    "见教程。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b624ebee-980f-4c1e-b963-a24ff0b669f6",
   "metadata": {},
   "source": [
    "### 3.任务分析\n",
    "\n",
    "求解思路如下：\n",
    "\n",
    "- 观察房屋类型可以看出该问题是一个二分类问题，房屋类型分为1类高档住宅和0类普通住宅；\n",
    "- 当输入一个房屋面积时，先根据模型中的w和b来计算出一个预测值y；\n",
    "- 把预测值y套入Sigmoid函数计算出一个概率值$\\hat{y}$，再根据设计的阈值，来对该概率值$\\hat{y}$进行解析；\n",
    "- 最后得出该样本所属的房屋类型。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "435c6090-cfda-4f46-a550-22a368e41e4a",
   "metadata": {},
   "source": [
    "### 4.任务实施\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "ec75eb6c-5da3-467d-a471-ca3b47242dd6",
   "metadata": {},
   "source": [
    "执行代码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2ae9da58-e339-4d22-9f8d-ca255711d89e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "迭代次数 1 ，损失： 0.8528074 ，准确率： 0.625\n",
      "迭代次数 2 ，损失： 0.40025944 ，准确率： 0.875\n",
      "迭代次数 3 ，损失： 0.34150386 ，准确率： 0.8125\n",
      "迭代次数 4 ，损失： 0.32257143 ，准确率： 0.8125\n",
      "迭代次数 5 ，损失： 0.31397203 ，准确率： 0.8125\n",
      "训练后的w： 0.08808256 训练后的b： -1.161462\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "# 处理中文乱码\n",
    "plt.rcParams['font.sans-serif']=['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "# 1，加载数据\n",
    "# 房屋面积\n",
    "x=np.array([137.97,104.50,100.00,126.32,79.20,99.00,124.00,114.00,106.69,140.05,53.75,46.91,68.00,63.02,81.26,86.21])\n",
    "# 房屋类型（1表示高档住宅，0表示普通住宅）\n",
    "y=np.array([1,1,0,1,0,1,1,0,0,1,0,0,0,0,0,0])\n",
    "# 2，数据预处理\n",
    "# 数据中心化，因为Sigmoid函数以0点为中心，将数据从-到+映射到0到1，因此需要进行中心化\n",
    "# 中心化之后，x_train的值围绕0点分布，经Sigmoid函数映射之后，映射为以0.5为中心的0到1之间的数\n",
    "x_train=x-np.mean(x)\n",
    "y_train=y    \n",
    "# 3，设置超参数\n",
    "# 学习率\n",
    "lr=0.005\n",
    "# 迭代次数\n",
    "iter=5\n",
    "# 显示频率\n",
    "display_step=1\n",
    "# 初始化模型参数\n",
    "np.random.seed(612)\n",
    "w=tf.Variable(np.random.randn())\n",
    "b=tf.Variable(np.random.randn())\n",
    "# 4，训练\n",
    "# 存放训练集的交叉熵损失\n",
    "cross_train=[]\n",
    "# 存放训练集的分类准确率\n",
    "acc_train=[]\n",
    "# 可视化\n",
    "plt.figure(figsize=(10,8))\n",
    "# 5，绘制样本散点图\n",
    "plt.subplot(221)\n",
    "plt.xlabel(\"特征\",fontsize='large')\n",
    "plt.ylabel(\"标签\",fontsize='large')\n",
    "plt.scatter(x_train,y_train)\n",
    "# 6，绘制Sigmoid曲线\n",
    "plt.subplot(222)\n",
    "plt.xlabel(\"特征\",fontsize='large')\n",
    "plt.ylabel(\"预测值\",fontsize='large')\n",
    "x_=range(-80,80)\n",
    "y_=1/(1+tf.exp(-(w*x_+b)))\n",
    "# 7，绘制初始曲线\n",
    "plt.plot(x_,y_, '--',linewidth=1)\n",
    "for i in range(iter):\n",
    "    with tf.GradientTape() as tape:\n",
    "        # 计算预测值（线性结果使用Sigmoid函数转换）\n",
    "        pred_train=1/(1+tf.exp(-(w*x_train+b)))\n",
    "        # 计算交叉熵损失\n",
    "        Loss_train=-tf.reduce_mean(y_train*tf.math.log(pred_train)+(1-y_train)*tf.math.log(1-pred_train))\n",
    "    # 将损失存入列表\n",
    "    cross_train.append(Loss_train)        \n",
    "    # 计算此时的准确率\n",
    "    Accuracy_train=tf.reduce_mean(tf.cast(tf.equal(tf.where(pred_train>0.5,1,0),y_train),tf.float32))\n",
    "    acc_train.append(Accuracy_train)    \n",
    "    # 求导\n",
    "    dL_dw,dL_db=tape.gradient(Loss_train,[w,b])\n",
    "    # 更新参数\n",
    "    w.assign_sub(lr*dL_dw)\n",
    "    b.assign_sub(lr*dL_db)\n",
    "   \n",
    "    # 打印损失和准确率\n",
    "    if i % display_step==0:\n",
    "        print(\"迭代次数\",i + 1,\"，损失：\",Loss_train.numpy(),\"，准确率：\", Accuracy_train.numpy())\n",
    "        \n",
    "print(\"训练后的w：\",w.numpy(),\"训练后的b：\",b.numpy())\n",
    "# 8，输出训练后的Sigmoid曲线\n",
    "y_=1/(1+tf.exp(-(w*x_+b)))\n",
    "plt.plot(x_,y_,linewidth=1)\n",
    "\n",
    "# 9，绘制Loss曲线\n",
    "plt.subplot(223)\n",
    "plt.xlabel(\"迭代次数\",fontsize='large')\n",
    "plt.ylabel(\"交叉熵损失\",fontsize='large')\n",
    "plt.plot(cross_train)\n",
    "# 10，绘制准确率\n",
    "plt.subplot(224)\n",
    "plt.xlabel(\"迭代次数\",fontsize='large')\n",
    "plt.ylabel(\"准确率\",fontsize='large')\n",
    "plt.plot(acc_train)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19958808-b43b-41d1-9f9f-e9258c128da0",
   "metadata": {},
   "source": [
    "可见，随着迭代次数增加，交叉熵损失递减，准确率递增。\n",
    "\n",
    "虚线为w和b是初始值的Sigmoid曲线；实线为w和b是训练后的值的Sigmoid曲线。随着训练次数的增加，输出概率越来越能够反映样本的真实分类。由于两类样本（高档住宅、普通住宅）有一定的交集，所以在这个区域中的点可能会出现分类错误，导致准确率无法达到百分之百。。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8866a3e3-cf5a-4961-a245-fabd30450fcf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "对110平方米房屋的预测值： 0.5249493\n",
      "110平方米房屋的类别为： 1\n"
     ]
    }
   ],
   "source": [
    "# 11，对新房屋进行分类\n",
    "x_new = 110 - np.mean(x)\n",
    "# 输出概率\n",
    "y_new=1/(1+tf.exp(-(w*x_new+b)))\n",
    "print(\"对110平方米房屋的预测值：\",y_new.numpy())\n",
    "# 以0.5为阈值，将概率转换成类别\n",
    "y_new=tf.where(y_new>0.5,1,0)\n",
    "print(\"110平方米房屋的类别为：\",y_new.numpy())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f24727c1-c726-43c0-bcb3-9ac399d5a83c",
   "metadata": {},
   "source": [
    "以0.5为阈值，对预测值进行转换，得到类别为1（高档住宅）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8f986030-a14c-49a5-89d2-3e764bc66822",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
